A currently leading technology for positioning applications in the automotive market is Global Navigation Satellite System (GNSS), with conventionally used navigation systems including navigation and telematics. In the near future, emerging applications such as autonomous driving, car to car (and to infrastructure) communication may provide for further technological challenges.
A GNSS receiver provides an absolute position reference, while possibly exhibiting one or more drawbacks. For example, a GNSS receiver relies on a satellite signal, which may be unavailable (e.g., indoor, tunnels) or corrupted (e.g., urban environments, reflections, and/or multipath).
In order to overcome these limitations, a solution may involve merging GNSS technology with other navigation technologies, to facilitate generating a redundant system wherein a component is able to overcome the disadvantages of one or other type of components.
Examples of technologies complementary to GNSS include inertial sensors, e.g., Inertial Measurement Units, (3-axes) gyroscopes, (3-axes) accelerometers, odometers, compasses, and/or barometers.
An aim of such merged or coupled systems, is therefore to exploit the features of each navigation technology in order to facilitate providing accurate information, possibly switching seamlessly to inputs from the GNSS system or the inertial system. The science studying methods to merge GNSS and other sensors, exploiting their complementary features, is called sensor fusion. In the automotive field, for example, sensor fusion frameworks may include Dead Reckoning (DR), where a GNSS system is complemented with a gyroscope (e.g., for yaw rate detection), an odometer or a velocity sensor (e.g., for a travelled path). Also, with the addition of a 3-axes accelerometer to the sensors, DR systems may become tri-dimensional (3D), i.e., a sensor-based height calculation may be possible.
Integrating a 3-axes accelerometer may lead to the development of another fusion technology: Inertial Navigation System (INS), based on calculation of travelled distance directly from an accelerometer, through a double-integration of measured accelerations. Although the processing framework therein appears to be complex, their functionality may be summarized stating that they calculate travelled distance from accelerometer by double integrating its signal. Such systems may provide position with increased availability and accuracy in respect of standalone GNSS receivers, and without vehicle connection (e.g., an odometer is virtually replaced by the accelerometer). Since odometer/velocity sensors are superfluous, the unit may be disconnected from the vehicle or may not receive any data from it, making it more suitable for e.g., aftermarket projects or situations where the final user or installer may find it difficult to retrieve a vehicle connection (which may be a confidential information exclusive of the vehicle producers).
Nevertheless, the accuracy level is inferior to connected DR systems due to the complex accelerometer signal integration. For example, the accelerometer does not only assess vehicle accelerations, but also gravitational acceleration, which means that potentially if one was able to completely separate the two accelerations, one would obtain an accurate travelled distance estimation.
However, such separation is not possible in practical application, and, although mitigated, a fraction of gravitational acceleration may occur in an integrated accelerometer signal, yielding to a velocity and position error increasing with time. Additionally, the Bill Of Materials (BOM) for such systems is higher than in the previous cases, e.g., the cost of combined GNSS, gyroscope and accelerometer.
A conventional positioning system may include one or more of the components described in the following. The positioning system may include a GNSS receiver block, implemented either in hardware or software, which may be used for receiving satellite signals from one or more Global Navigation Satellite Systems (GNSS), for calculating absolute user position, velocity, heading, travelled distance and time, and for transmitting raw GNSS measurements (pseudo-ranges and frequency of tracked SV—Satellite Vehicles) and/or Position Velocity and Time (PVT).
Also, an Internal Measurement Unit (IMU) may be provided, including e.g., a 3-axes gyroscope and/or a 3-axes accelerometer, which may be composed by two separate sensors (3+3-axes) or by a single package containing both sensors (6 axes). The block may be implemented in hardware and it may include a 3-axes gyroscope, used to assess angular rates, and a 3-axes accelerometer, used to assess accelerations (the axes of each sensor being perpendicular to one another, i.e., oriented like a classic xyz reference frame). Independently of its arrangement, an output of the IMU block may be generated, at each epoch, by the motion of a vehicle on which it is mounted, e.g., using three angular rates and three accelerations.
These measures are referred to as “raw” sensor data, which may imply that: the data are not calibrated for known error sources (conventionally, sensitivity and bias); and/or the data are related to a reference frame centered and fixed in the sensor itself, conventionally referred to as sensor (or body) frame.
With respect to angular rates and accelerations physically acting on the vehicle, the angular rates and accelerations which may be relevant to a navigation application are related to a frame centered and fixed to the vehicle; the frame being conventionally referred to as vehicle (or navigation) frame.
As previously mentioned, GNSS and IMU systems may be integrated in the same device, insofar as the benefit of one compensates the drawbacks of the other: in fact, GNSS is a technology with an accuracy possibly influenced by environmental aspects (e.g., reflected signal), hence being highly space correlated and poorly time correlated. In other words, GNSS may be inaccurate in a short term scenario (or at high frequencies), but it may be accurate in a long term scenario (or at low frequencies).
On the other hand, IMU and in general motion sensors show inherent errors, independent from the surrounding scenario (e.g., inaccuracy in calibration), causing an error that integrates over time; they are accurate short term (at high frequencies) and may diverge in a long term scenario (at low frequencies).
Another component that may be included in a DR system (e.g., connected to a vehicle) is the vehicle interface processor, introduced to manage a connection with, while possibly decoding, vehicle data.
The outputs of IMU and GNSS components flow into a main processor block, which implements, either in hardware or in software, an operation which is referred to as sensor fusion, i.e., merging the contribution of each block in order to overcome the limitations of each technology and generating a (possibly seamless) integration of the two, with the aim of providing as an output continuous and accurate position, velocity/speed and attitude. The quantities (i.e., position, velocity and attitude) may then be transferred from the main processor block to its final application.
For example, in case of a navigation system, the final application may include a mapping unit aimed to match a calculated position on a map, and use it to guide a user toward a selected destination. In a telematics system, a PVT output may be sent via a wireless link (e.g., 2G, 3G, Wi-Fi, BT) to a service provider, such as an insurance company which aims to monitor the travelled distance or the driving style or a transportation company which aims to track its fleet, or other internet of things (IoT) style examples.
As previously mentioned, the most popular way to provide a standalone positioning capability is a GNSS receiver, namely a unit/box capable of receiving (via an antenna) GNSS signals and of using them to calculate a user position, velocity and time. However, GNSS receivers are accurate only as long as the signal conditions are good.
Moreover, the cost of the GNSS receiver may vary depending on features (i.e., precise positioning/RTK—Real Time Kinematic—raise the cost). However the BOM of a standalone solution may be conventionally lower than a BOM for a solution integrated with other technologies. On the other hand, such standalone units may exhibit the drawback of being highly inaccurate in reflective or obstructed environments (e.g., urban scenarios) and their service may be unavailable indoor (e.g., in tunnel or parking areas) where GNSS signals are difficult to receive.
As previously mentioned, to overcome standalone GNSS limitations, position units including DR technology were introduced, including components as set forth previously. The unit augments performance through the integration of angular sensors (e.g., gyroscope, differential velocities) and distance sensors (e.g., odometer, tachymeter). This makes it possible to provide an accurate position independently from GNSS availability, because, in the (at least partial) absence of satellite signals, such units continue, possibly seamlessly, to provide position calculating the relative motion using as a starting point a last valid GNSS calculated point, with distance and angular sensors providing linear and angular motion, respectively.
In this case, the positioning quality depends on the quality of the sensors, insofar as the accuracy of a relative motion depends solely on type and calibration thereof, i.e., on sensor grade: military-grade IMUs (i.e., embedding gyroscopes and accelerometers) may show virtually no error, while mid/low cost sensors conventionally used in commercial applications exhibit a progressive error accumulation when GNSS is unavailable.
Another drawback of DR includes the use of a wired connection to a vehicle to calculate a travelled distance, conventionally assessed through sensors of wheel motion (e.g., a magnetic encoder). In particular, there are two conventional techniques that may be used to transfer data across a vehicle:
Analog or Discrete Odometer: an analog signal transmits raw pulses generated by the wheel encoder sensor, i.e., velocity is directly proportional to pulses rate of the sensors; and/or
a standard cable which may be connected to an electronic device through a standard General Purpose I/O (GPIO), e.g., it may be loaded on a vehicle information system such as the Controller Area Network (CAN) bus. In this case, the information is pre-processed and encoded in a binary format, proprietary of the car maker (the velocity, on a CAN bus, may be already pre-calculated).
Concerning the automotive field, such a limitation may be detrimental for after-market and commercial applications (e.g., telematics insurance boxes, vehicle tracking systems), where the positioning unit is sold separately from the vehicle itself and may be installed on the vehicle either by a third party or the user itself. Under these premises, it may be difficult to connect the unit to the travelled path source of the vehicle. For example, in case of an analog odometer, the point that may be used to connect with a cable may be unknown, or it may change for different manufacturers and/or vehicles.
Also, connecting to the cable may involve unmounting a part of the vehicle dash, an operation that is hardly suited to an end user and may require the involvement of a professional with a specific background. This is not only costly, but it may also be detrimental for a consumer aftermarket application (e.g., if buying a device implies tweaking a car, then an end user might not want to buy it).
Also, in case of CAN bus, even if the CAN bus is accessed, the information is possibly encrypted; a decoding key is a proprietary information of a car manufacturer, that may be conservative and not willing to disclose it, in order not to provide an advantage to competition. Information may be shared, e.g., with key industrial partners which may imply an original equipment manufacturer (OEM) business model. In conclusion, for an aftermarket or consumer application, it may be difficult to obtain access to vehicle data.
In addition to this, the BOM for such systems appears to be higher than for GNSS systems, as the IMU poses an additional cost.